U.S. patent application number 15/476391 was filed with the patent office on 2018-10-04 for methods and apparatus for determining biological effects of environmental sounds.
The applicant listed for this patent is Intel Corporation. Invention is credited to Yuri I. Krimon, David I. Poisner, Monika S. Sane.
Application Number | 20180279962 15/476391 |
Document ID | / |
Family ID | 63671536 |
Filed Date | 2018-10-04 |
United States Patent
Application |
20180279962 |
Kind Code |
A1 |
Sane; Monika S. ; et
al. |
October 4, 2018 |
METHODS AND APPARATUS FOR DETERMINING BIOLOGICAL EFFECTS OF
ENVIRONMENTAL SOUNDS
Abstract
Methods and apparatus for determining biological effects of
environmental sounds are disclosed. An example apparatus includes a
sound characteristic analyzer to identify a sound event based on
audio data in an environment. The example apparatus includes a
physiological data analyzer to identify a physiological event based
on physiological response data collected from a user exposed to the
sound event in the environment. The example apparatus includes a
correlation identifier to identify a correlation between the sound
event and the physiological event and a report generator to
generate a report based on the correlation.
Inventors: |
Sane; Monika S.; (Folsom,
CA) ; Poisner; David I.; (Carmichael, CA) ;
Krimon; Yuri I.; (Folsom, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
63671536 |
Appl. No.: |
15/476391 |
Filed: |
March 31, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0531 20130101;
A61B 5/0205 20130101; A61B 5/0816 20130101; G16H 50/70 20180101;
G16H 15/00 20180101; A61B 5/024 20130101; G16H 50/20 20180101; A61B
5/7275 20130101; A61B 5/7282 20130101; A61B 5/7246 20130101; A61B
5/021 20130101; G16H 40/63 20180101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0205 20060101 A61B005/0205 |
Claims
1. An apparatus comprising: a sound characteristic analyzer to
identify a sound event based on audio data collected in an
environment; a physiological data analyzer to identify a
physiological event based on physiological response data collected
from a user exposed to the sound event in the environment; a
correlation identifier to identify a correlation between the sound
event and the physiological event; and a report generator to
generate a report based on the correlation.
2. The apparatus as defined in claim 1, wherein the sound
characteristic analyzer is to identify the sound event based on a
sound characteristic of the audio data.
3. The apparatus as defined in claim 2, wherein the sound
characteristic includes one or more of amplitude, pitch, frequency,
attack, or a duration of a sound in the audio data.
4. The apparatus as defined in claim 1, wherein the physiological
response data includes one or more of heart rate data, respiration
rate data, blood pressure data, or skin conductivity data.
5. The apparatus as defined in claim 1, wherein the audio data is
first audio data and the correlation identifier is to: perform a
comparison of the first audio data to second audio data; and detect
a change in a sound characteristic of the first audio data relative
to the sound characteristic in the second audio data, the
correlation identifier to identify the correlation based on the
change in the characteristic.
6. The apparatus as defined in claim 5, wherein the correlation
identifier is to identify an attenuation or a gain of the first
audio data relative to the second audio data and adjust the
correlation based on the attenuation or the gain.
7. The apparatus as defined in claim 1, wherein the user is a first
user, the physiological event is a first physiological event, and
the correlation is a first correlation, the correlation identifier
to identify a second correlation between the sound event and a
second physiological event associated with a second user different
from the first user.
8. The apparatus as defined in claim 7, further including: a sound
comparer to perform a comparison of the sound event to a reference
sound event; and a crowd source analyzer to identify the sound
event as affecting the first user and the second user based on the
first correlation, the second correlation, and the comparison, the
report generator to transmit a request to a third party based on
the identification of the sound event as affecting the first user
and the second user.
9. The apparatus as defined in claim 1, wherein the report
generator is to automatically place an order for a noise reduction
device for the user.
10. A method comprising: identifying, by executing an instruction
with a processor, a sound in an audio stream collected in an
environment; identifying, by executing an instruction with the
processor, a physiological event based on physiological response
data collected from a user exposed to the sound in the environment;
determining, by executing an instruction with the processor, a
correlation between the sound and the physiological event; and
generating, by executing an instruction with the processor, a
report based on the correlation.
11. The method as defined in claim 10, wherein the audio stream is
a first audio stream and further including: performing a comparison
of the first audio stream to a second audio stream; detecting a
change in a sound characteristic of the first audio stream relative
to the sound characteristic in the second audio stream; and
identifying the correlation based on the change in the
characteristic.
12. The method as defined in claim 10, wherein the user is a first
user, the physiological event is a first physiological event, and
the correlation is a first correlation, and further including
identifying a second correlation between the sound and a second
physiological event associated with a second user different from
the first user, wherein generating the report includes generating a
first report based on the first correlation and a second report
based on the second correlation.
13. The method as defined in claim 10, wherein the user is a first
user and further including identifying the correlation based on
previously collected physiological response data for the first user
or for a second user.
14. At least one computer readable storage medium comprising
instructions that, when executed, cause a machine to at least:
detect a sound event in audio data collected in an environment;
detect a physiological event in physiological response data
collected from a user exposed to the sound event in the
environment; identify a correlation between the sound event and the
physiological event; and generate an instruction based on the
correlation.
15. The at least one computer readable storage medium as defined in
claim 14, wherein the instructions, when executed, further cause
the machine to identify the sound event based on one or more of
amplitude, pitch, frequency, attack, or a duration of a sound in
the audio data.
16. The at least one computer readable storage medium as defined in
claim 14, wherein the audio data is first audio data and wherein
the instructions, when executed, further cause the machine to:
perform a comparison of the first audio data to second audio data;
detect a change in a sound characteristic of the first audio data
relative to the sound characteristic in the second audio data; and
identify the correlation based on the change in the
characteristic.
17. The at least one computer readable storage medium as defined in
claim 16, wherein the instructions, when executed, further cause
the machine to identify an attenuation or a gain of the first audio
data relative to the second audio data and adjust the correlation
based on the attenuation or the gain.
18. The at least one computer readable storage medium as defined in
claim 14, wherein the instructions, when executed, further cause
the machine to: analyze a user input indicating that the user
employs a noise reduction device; and adjust the correlation based
on the user input.
19. The at least one computer readable storage medium as defined in
claim 14, wherein the user is a first user, and wherein the
instructions, when executed, further cause the machine to identify
the correlation based on previously collected physiological
response data for the first user or for a second user.
20. The at least one computer readable storage medium as defined in
claim 14, wherein the instructions, when executed, further cause
the machine to generate the instruction by automatically placing an
order for a noise reduction device for the user.
Description
FIELD OF THE DISCLOSURE
[0001] This disclosure relates generally to environmental sound
analysis and, more particularly, to methods and apparatus for
determining biological effects of environmental sounds.
BACKGROUND
[0002] An individual is exposed to many different environmental
sounds on a daily basis, including, for example, sounds generated
by traffic, machines, music playing, people talking, etc. Some of
the sounds the individual encounters in an environment are
sustained. For example, an individual working in a factory is
exposed to sounds generated by machinery for an extended period of
time over the work day. Other sounds are sudden, such as a loud
explosion when the individual walks by a construction site.
Exposure to different sounds affects an individual physiologically
and psychologically.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 illustrates an example system constructed in
accordance with the teachings disclosed herein including a
biological data collection device, an audio collection device for
collecting environmental sounds, and a networked sound impact
analyzer for determining the biological effects of the sounds.
[0004] FIG. 2 is a block diagram of an example implementation of
the sound impact analyzer of FIG. 1.
[0005] FIG. 3 is a flowchart representative of example machine
readable instructions that may be executed to implement example
systems of FIGS. 1 and/or 2.
[0006] FIG. 4 illustrates an example processor platform that may
execute the example instructions of FIG. 3 to implement example
systems of FIGS. 1 and 2.
[0007] The figures are not to scale. Wherever possible, the same
reference numbers will be used throughout the drawing(s) and
accompanying written description to refer to the same or like
parts.
DETAILED DESCRIPTION
[0008] On a daily basis, an individual is exposed to many different
sounds emanating from different environments. For example, in a
work environment such as a factory or office, the individual may be
exposed to sounds generated by machinery, people talking, music
playing, etc. When the individual is outside, the individual may be
exposed to sounds generated by traffic, construction equipment,
etc. In some examples, the individual may be exposed to infrasonic
sounds, or sounds occurring at frequency levels below the human
hearing range (e.g., below 20 Hz). Infrasonic sounds can stem from
nature, such as earthquakes, or man-made sources, such as trucks,
aircraft, etc. In some examples, the individual may be exposed to
ultrasonic sounds, or sounds occurring at frequency levels above
the human hearing range (e.g., above 20,000 Hz). Ultrasonic sounds
can include certain animal whistles (e.g., dog whistles) or sonar
emissions.
[0009] Exposure to different sounds--whether sustained, sudden,
infrasonic, ultrasonic, etc.--may affect individuals
physiologically and/or psychologically. Different individuals
respond differently to different sounds. Characteristics of sound
such as pitch, amplitude, duration, pattern, attack (e.g., a way in
which a sound is initiated, where the sound of gunshot has a fast
attack and the sounds of tearing a sheet of paper has a slow
attack) can affect physical biological parameters such as heart
rate and blood pressure. Further, individuals may have different
psychological responses to sound. For example, a first individual
may consider a sound to be noise (e.g., unwanted sound), while a
second individual may consider the sound to be pleasant. Thus,
physiological and/or psychological effects of sound on individuals
can differ from no effect to, for example, hearing loss and/or
stress.
[0010] Although some audio media players such as smartphones
display warnings when a user raises the volume to alert the user to
the risk of hearing damage, such warnings are based on decibel
levels. Thus, such sound measurements do not account for other
characteristics of sound, such as pattern and duration. Further,
generic warnings based on decibel levels do not correlate sound
with the physiological and/or psychological effects of the sound on
the user. Moreover, such warnings do not account for different user
responses to different sounds and, thus, are not user-specific.
[0011] Example systems and methods disclosed herein analyze audio
collected from an environment and physiological response data
collected from a user exposed to the environment. In some examples,
the physiological response data is collected while the user is in
the environment. In some examples, the physiological response data
is additionally or alternatively collected after the user is
removed from the environment. Based on the analysis of the audio
and the physiological response data, examples disclosed herein
correlate sound events with the physiological response data to
identify the effects of sound on the user. Some examples identify
effect(s) of specific sounds, such as an explosion, on the user's
physiological response(s). Other examples identify effect(s) of
sustained or repeated sounds on a user, such as daily exposure to
machinery sounds in a factory, based on historical tracking of
audio and physiological responses. Some examples combine survey
data obtained from the user with the physiological response data to
assess the effect(s) of the sound(s) on the user physiologically
and psychologically.
[0012] Disclosed examples collect (e.g., record) audio content in
an environment via a microphone associated with, for example, a
smartphone, a wearable device, and/or a stand-alone speaker/audio
sensor device (e.g., Amazon.TM. Echo.TM.). The audio is wirelessly
transmitted to a networked analyzer (e.g., a server, one or more
processors, etc.) via an application (an app) executed on the
microphone-enabled user device. Disclosed examples monitor a user's
physiological responses such as heart rate, blood pressure, and/or
respiration rate via one or more sensors of a wearable device worn
by the user. The physiological response data is wirelessly
transmitted to the analyzer. In some examples, the wearable device
and the microphone-enabled device are the same device.
[0013] Based on the audio collected from the environment and the
physiological data gathered from the user, examples disclosed
herein determine correlations between the audio and the user's
physiological response. Some such examples generate one or more
outputs for presentation to the user via the user device
application such as, for example, information about the user's
hearing capacity and/or the user's daily exposure to the audio,
personalized recommendations for audio level settings, etc. Some
examples provide data to one or more third parties such as an
authorized medical professional for tracking, for example, hearing
loss.
[0014] In some examples, a plurality of users are located in an
environment such as a factory from which one or more sounds are
collected as audio data. Physiological data is collected from all
or some of the users and transmitted to the analyzer. The analyzer
identifies correlations between the sounds in the environment
(e.g., in the building) and the users' physiological response data.
Some such examples provide outputs to, for example, building
managers with respect to the effects of sounds from equipment,
elevators, etc. on the users. In some examples, substantially
similar changes in physiological responses may be detected across
users in substantially real-time corresponding to the detection of
a specific (e.g., sudden) sound event. In such examples, the
correlation between the similar changes in physiological responses
across the users and the specific sound is used to determine that
there has been a crowd-impacting event such as an explosion. Thus,
disclosed examples may provide for sound event detection and/or
customized warnings based on audio data and physiological data
collected from one or more users.
[0015] FIG. 1 illustrates an example system 100 constructed in
accordance with the teachings of this disclosure for determining
the biological effect(s) of sound on a user exposed to audio in an
environment. The example system 100 can be implemented in any
environment 102. In the example system 100 of FIG. 1, a first user
104, a second user 106, and a third user 108 are exposed to
sound(s) within the environment 102. Additional or fewer users can
be present in the environment 102.
[0016] The environment 102 can be, for example, an indoor setting
such as a building (e.g., a factory, an office building, a home,
etc.) or an outdoor setting (e.g., an amusement park, a
construction site, an airfield, etc.). In some examples, the
environment 102 is based on a location of a particular user (e.g.,
one of the first, second, or third users 104, 106, 108). For
example, the environment 102 can be defined by one or more
locations that the first user 104 moves between, such as home, a
city street, an office building, etc.
[0017] The user(s) 104, 106, 108 are exposed to audio 110 while in
the environment 102. The audio 110 can include sound(s) generated
by traffic, machines, voices, music, etc. In some examples, the
audio 110 includes humanly audible sounds, infrasonic sounds (e.g.,
low-frequency sounds below the human hearing range) and/or
ultrasonic sounds (e.g., high-frequency sounds above the human
hearing range). In some examples, the audio 110 includes sudden
sound(s) (e.g., a loud crash) or sustained sound(s) (e.g., sound(s)
generated by a machine running for a duration of the work day). The
audio 110 in the environment 102 can include one or more sound(s)
having different or similar characteristics with respect to pitch,
amplitude, duration, attack, pattern, etc.
[0018] In the example of FIG. 1, the audio 110 is collected (e.g.,
recorded) by a microphone-enabled device and transmitted to a sound
impact analyzer 112. In the example of FIG. 1, the sound impact
analyzer 112 is implemented by one or more cloud-based device(s)
such as one or more servers, processor(s), and/or virtual
machine(s). In other examples, some of the analysis performed by
the sound impact analyzer 112 is implemented by the cloud-based
device(s) and other parts of the analysis are implemented by
processor(s) of one or more user device(s) (e.g., smartphones).
[0019] In the example of FIG. 1, physiological response data is
collected from each of the users 104, 106, 108 and transmitted to
the sound impact analyzer 112. For ease of discussion, the
collecting of the audio 110 and the collection of the physiological
response data from the users 104, 106, 108 may be discussed in
connection with the first user 104 with the understanding that the
same or similar description apply to the second user 106 and/or the
third user 108 in substantially the same manner.
[0020] In the example of FIG. 1, one or more microphones 114 are
disposed in the environment 102 to collect the audio 110. The
microphone(s) 114 can be associated with a user device 116 (e.g.,
user device(s) of any or all of the first user 104, the second user
106, and/or the third user 108). The user device 116 can be
implemented by a smartphone, a tablet, etc. In other examples, the
user device 116 is a stand-alone speaker/audio sensor device
located in the environment 102, such as the Amazon.TM. Echo.TM. or
Google.TM. Home.TM.. In some examples, the microphone(s) 114 are
associated with wearable device(s) 118, such as a watch, glasses, a
wearable walkie-talkie, etc. The wearable device(s) 118 may be worn
by any or all of the users 104, 106, 108. In some examples, the
microphone(s) 114 are implemented by a Bluetooth microphone
associated with a Bluetooth-enabled user device. For illustrative
purposes, the microphone(s) 114 are shown as associated with each
of the user devices 116 in FIG. 1 but, in practice, each user
device 116 need not have a microphone and/or the microphone(s) can
be associated with a different device.
[0021] In the example of FIG. 1, the user device 116 includes a
processor 115. The processor 115 executes a first user application
120. The first user application 120 instructs the microphone(s) 114
to collect the audio 110 in the environment 102. In some examples,
the first user application 120 instructs the microphone(s) 114 to
collect the audio 110 for a predefined time period, such as while
the first user application 120 is running. In other examples, the
first user application 120 instructs the microphone(s) 114 to
collect the audio 110 based on one or more user inputs received via
the user device 116 to start and stop the audio collecting.
[0022] The processor 115 of the user device 116 is in communication
with a memory 121. In the illustrated example, the memory 121
stores a database 122. The database 122 includes one or more rules
123 with respect to the collection of the audio 110. For example,
the rule(s) 123 identify one or more event(s) and/or threshold(s)
that trigger recording of the audio 110. In some such examples, the
microphone(s) may be "always on" in that they always collect audio.
This audio may be buffered in the memory 121 temporarily. The audio
may be discarded and/or overwritten unless an event occurs as
defined in the rule(s) 123 (e.g., unless a threshold is satisfied).
In some examples, the threshold includes an amplitude level. In
such examples, the audio 110 exported to the sound impact analyzer
112 and/or preserved for such exportation if the audio 110
surpasses the threshold amplitude level. In other examples, the
threshold is based on one or more other characteristics of the
audio 110, such as a pattern of the sound and/or a duration of the
sound. In some examples, the threshold for exporting and/or
preserving the audio 110 for exporting is based on a location of
the user 104, 106, 108 (e.g., as detected by a GPS 124 of the user
device 116) or a time of day (e.g., as detected by a clock 125 of
the user device 116). In some examples, the microphone(s) 114 may
only collect audio when in the noted location(s) and/or during the
noted time(s) of day (e.g., the microphone(s) 114 are not "always
on" but instead are activated for audio collection only when the
defined conditions are met). The thresholds can be set by the user
104, 106, 108 and/or a third party such as a medical professional.
The rule(s) 123 can also include settings with respect to the
duration of time the audio 110 should be recorded, a digital format
for the recording, a time at which the data should be exported, an
amount of data that is presented for exporting, etc.
[0023] The example first user application 120 of FIG. 1 generates
an audio stream 126 based on the audio 110 collected by the
microphone(s) 114 that is to be exported to the sound impact
analyzer 112. In some examples, the audio stream 126 is stored in
the memory 121 or a buffer. The example user device 116 of FIG. 1
is in communication (e.g., wireless communication) with the sound
impact analyzer 112. The user device 116 may transmit the audio
stream 126 to the sound impact sound impact analyzer 112 using any
past, present, or future communication protocol. In some examples,
the user device 116 transmits the audio stream 126 to the sound
impact analyzer 112 in substantially real-time as the audio stream
126 is generated. In other examples, the user device 116 transmits
the auto stream 126 to the sound impact analyzer 112 at a later
time (e.g., based on one or more settings such as a preset time of
transmission, an amount of data buffered, availability of WiFi,
etc.).
[0024] In some examples, the user 104, 106, 108 provides one or
more user inputs 127 via the first user application 120. The user
input(s) 127 can include preferences with respect to, for example,
the collection, buffering, storage, and/or recording of the audio
stream(s) 126 by the user device 116. The user input(s) 127 can
include data such as whether the corresponding user 104, 106, 108
is wearing or, more generally, associated with a noise reduction
device (e.g., the user is wearing ear plugs, the user is located in
a sound-proof room in the environment). In some examples, the first
user application 120 periodically or aperiodically presents the
user 104, 106, 108 with one or more surveys 134 such as whether
he/she heard a specific sound collected by the microphone(s) 114
(e.g., based on a decibel level threshold defined by the rule(s)
123). The surveys 134 can include, for example, questions about the
user's physiological responses to the sound(s), such as whether the
user 104, 106, 108 was frightened. The surveys 134 can be generated
by the first user application 120 and/or the sound impact analyzer
112. The user 104, 106, 108 can provide the user input(s) 127 via
the user device 116 (e.g., via a display screen of the user device
116 or via another interface). In the example of FIG. 1, the user
device 116 transmits the user input(s) 127 to the sound impact
analyzer 112.
[0025] In the example of FIG. 1, each user 104, 106, 108 wears a
wearable device 118. As disclosed herein, the wearable device 118
can be a watch, glasses, a wearable walkie-talkie, etc. The example
wearable device 118 of FIG. 1 includes one or more sensors 128. The
sensor(s) 128 measure one or more physiological parameters of the
corresponding user 104, 106, 108, such as heart rate, blood
pressure, arterial stiffness (e.g., as an index for blood
pressure), skin conductivity, respiration rate, respiration
pattern, etc. The sensor(s) 128 generate physiological response
data 130 based on the measurements. In some examples, the sensor(s)
128 measure the physiological parameters while the corresponding
user 104, 106, 108 wearing the device 118 is in the environment
102. In other examples, the sensor(s) 128 measure the physiological
parameters after the user 104, 106, 108 has left environment 102.
In some examples, the sensor(s) 128 measure the physiological
parameters while the user 104, 106, 108 is in the environment 102
and for a period of time after the user leaves from the environment
102.
[0026] The example wearable device(s) 118 include a processor 129.
The processor 129 of this example executes a second user
application 131. The second user application 131 is used to
control, for example, the collection of the physiological response
data 130 via the sensor(s) 128 and/or the exportation of the data
for the wearable device 118. The physiological response data 130
can be stored in a database 132 implemented by a memory 133 in
communication with the processor 129.
[0027] The example wearable device 118 of FIG. 1 is in
communication (e.g., wireless communication) with the sound impact
analyzer 112 of FIG. 1. The example wearable device 118 transmits
the physiological response data 130 to the sound impact analyzer
112. In some examples, the wearable device 118 transmits the
physiological response data 130 to the sound impact analyzer 112 in
substantially real-time as the physiological response data 130 is
generated. In other examples, the wearable device 118 transmits the
physiological response data 130 to the sound impact analyzer 112 at
a later time (e.g., periodically and/or aperiodically based on one
or more settings).
[0028] In some examples of the system 100 of FIG. 1, the wearable
device 118 and the user device 116 are integrated into one device.
For example, the processor 129 of the wearable device 118 can
include the microphone(s) 114. The processor 129 of the wearable
device can implement the first user application 120. In such
examples, the wearable device 118 transmits the physiological
response data 130, the audio stream 126, and/or the user input(s)
127 to the sound impact analyzer 112.
[0029] As illustrated in FIG. 1, the example sound impact analyzer
112 receives respective physiological response data 130 from the
wearable device(s) 118 worn by the respective users 104, 106, 108.
In some examples, the sound impact analyzer 112 receives also
respective audio streams 126 from the user devices 116 associated
with the different users 104, 106, 108 (e.g., smartphones). In
other examples, the audio stream 126 is transmitted to the sound
impact analyzer 112 via only one of the user devices 116 (e.g., the
user device 116 associated with the first user 104). For example,
if the first, second, and third users 104, 106, 108 are located in
the same room, the audio stream 126 generated from the audio 110
may be collected by the microphone(s) 114 of the user devices 116
associated with all of the users 104, 106, 108. However, the audio
stream 126 represents the audio to which all of the users 104, 106,
108 are exposed, so only one of the devices 116 need report the
audio to the sound impact analyzer 112.
[0030] The example sound impact analyzer 112 analyzes the audio
stream(s) 126 and the physiological response data 130 from the
first, second, and/or third users 104, 106, 108 to correlate the
physiological responses of the user(s) with sound event(s) in the
audio stream(s) 126. For example, the sound impact analyzer 112 may
correlate a change (e.g., an increased heart rate) detected in the
physiological response data 130 collected from the user 104, 106,
108 over a time period with a sound event detected in the audio
stream 126 (e.g., an increase in amplitude) over the same time
period. In some examples, the sound impact analyzer 112 tracks
changes in the physiological response data 130 compared to
previously collected or historical physiological response data 130
for the user 104, 106, 108. In such examples, the sound impact
analyzer 112 may correlate the changes in the physiological
response data 130 to sustained or repeated exposure to sounds based
on the data in the audio stream 126. In some examples, the sound
impact analyzer 112 analyzes the physiological response data 130
for two or more of the first, second, and third users 104, 106, 108
relative to the audio stream(s) 126. In such examples, the sound
impact analyzer 112 identifies the effect(s) of sound event(s) in
the audio stream(s) 126 across two or more users based on, for
example, similar changes identified in the physiological response
data for the corresponding users. In some examples, the sound
impact analyzer 112 receives user survey data collected from the
user(s) 104, 106, 108. In some such examples, the sound impact
analyzer 112 accounts for the psychological responses of the
user(s) 104, 106, 108 with respect to identifying correlations
between the audio 110 and the physiological response data 130.
[0031] In some examples, the sound impact analyzer 112 of FIG. 1
generates one or more surveys 134 based on the analysis of the
audio stream(s) 126 and the physiological response data 130 from
the first, second, and/or third user(s) 104, 106, 108. As disclosed
above, in the example of FIG. 1, the survey(s) 134 are presented to
the user(s) 104, 106, 108 via the user device(s) 116.
[0032] The example sound impact analyzer 112 generates one or more
reports or instructions 136 based on the analysis of the audio
stream(s) 126, the physiological response data 130 from the user(s)
104, 106, 108, and/or the user input(s) 127 in response to the
survey(s) 134 indicative of psychological responses of the user(s)
104, 106, 108 to the sound(s) in the environment 102. The report(s)
136 can include, for example, personalized recommendations for
audio levels for the user(s) 104, 106, 108 based on the
physiological response data, alerts regarding danger(s) or
potential ill effects of prolonged exposure to the audio 110,
information regarding the user's hearing capacity, etc. In some
examples, the report(s) 136 include information about noise sources
in the environment 102 that may be causing certain physiological
response(s) in the user(s) in the environment. For example, the
report(s) 136 can indicate whether the user(s) 104, 106, 108 are
experiencing adverse physiological responses to a machine that
generates a sustained operational sound and is located in the same
room as the user(s) 104, 106, 108. The report(s) 136 can indicate
whether the user(s) 104, 106, 108 are experiencing adverse
physiological responses to the sound(s) in the environment 102,
such as stress and/or anxiousness. The report(s) 136 can be
presented in, for example, a visual format, an audio format, and/or
another format (e.g., as a vibrating alert).
[0033] In some examples, the report(s) 136 include one or more
instructions to be executed by the sound impact analyzer 112 or one
or more other processors (e.g., the processor 115 of the user
device 116, the processor 129 of the wearable device 118). For
example, the report(s) 136 can include instruction(s) for an audio
playing device in the environment 102 to automatically reduce a
volume at which the audio 110 is played by the device in view of
the physiological and/or psychological effects of sound exposure on
the user(s) 104, 106, 108.
[0034] In the example system 100 of FIG. 1, the sound impact
analyzer 112 is in communication with one or more report
presentation devices 138. The report presentation device(s) 138 can
include the user device(s) 116 and/or the wearable device(s) 118
associated with the user(s) 104, 106, 108 (e.g., display screen(s)
of the device(s) 116, 118). In some examples, the report
presentation device(s) 138 include user devices (e.g., tablets,
smartphones, a personal computer) associated with a third party
authorized to receive the report(s) 136, such as a medical
professional, a parent, a building manager (e.g., of a factory
building, an employer, etc.). In examples where the report(s) 136
include instruction(s) for execution, the report presentation
device(s) 138 may execute the instructions to, for example, reduce
sound in the environment.
[0035] FIG. 2 is a block diagram of an example implementation of
the example sound impact analyzer 112 of FIG. 1. As mentioned
above, the example sound impact analyzer 112 is constructed to
correlate physiological and/or psychological responses of a user
(e.g., the first, second, and/or third users 104, 106, 108 of FIG.
1) with sound events in the audio stream(s) 126 representing
sound(s) occurring in the environment 102. In the example of FIG.
2, the sound impact analyzer 112 is implemented by one or more
servers and/or processor(s) located remotely from the users. In
some examples, the sound impact analyzer 112 is implemented by one
or more virtual machines in a cloud-computing environment. In other
examples, the sound impact analyzer 112 is implemented by one or
more of the processor 115 of the user device 116 and/or the
processor 129 of the wearable device 118. In other examples, some
of the sound impact analysis is implemented by the sound impact
analyzer 112 (e.g., via a cloud-computing environment) and one or
more other parts of the analysis is implemented by the processor
115 of the user device 116 and/or the processor 129 of the wearable
device 118.
[0036] The example sound impact analyzer 112 of FIG. 2 includes a
database 200. In other examples, the database 200 is located
external to the sound impact analyzer 112 in a location accessible
to the analyzer. As disclosed above, the audio stream(s) 126
corresponding to the audio 110 occurring in the environment 102 of
FIG. 1 are transmitted to the sound impact analyzer 112. Similarly,
the physiological response data 130 collected from the user is also
transmitted to the sound impact analyzer 112. Also, in some
examples, the user input(s) 127 in response to the survey(s) 134
(e.g., data indicative of physiological responses of the user) are
transmitted to the sound impact analyzer 112. The database 200
stores the audio stream(s) 126, the physiological response data
130, and the user input(s) 127. In some examples, the database 200
stores the audio stream(s) 126 and/or the physiological response
data 130 over time to generate historical audio data and/or
historical physiological data, respectively.
[0037] The example sound impact analyzer 112 of FIG. 2 includes a
sound characteristic analyzer 202. The sound characteristic
analyzer 202 receives and/or otherwise retrieves the audio
stream(s) 126 and processes the audio data included in the
stream(s). The sound characteristic analyzer 202 can perform one or
more operations on the audio data such as filtering the raw signal
data, removing noise from the signal data, converting the signal
data from analog data to digital data, converting time domain audio
data into the frequency spectrum (e.g., via Fast Fourier processing
(FFT)) for spectral analysis, and/or analyzing the data. In some
examples, the audio data is converted from analog to digital before
being delivered to the sound impact analyzer 112.
[0038] The example sound characteristic analyzer 202 of FIG. 2
analyzes the audio data of the audio stream(s) 126 with respect to
one or more characteristics of sound(s) in the audio stream(s) 126,
such as amplitude, frequency, pitch, duration, and/or attack. The
sound characteristic analyzer 202 detects, for example, changes in
the data with respect to one or more of the sound characteristics.
For example, the sound characteristic analyzer 202 identifies
change(s) in amplitude if a sound represented in the audio content
stream data. As another example, the sound characteristic analyzer
202 identifies change(s) in pitch over time of sound(s) in the
audio data. As another example, the sound characteristic analyzer
202 may detect an increase or decrease in a duration of a
characteristic or event in the audio data (or a portion thereof)
relative to, for example, previously collected audio data. The
sound characteristic(s) (e.g., amplitude, pitch, duration, etc.)
analyzed by the sound characteristic analyzer 202 can be defined by
one or more user inputs.
[0039] Based on the analysis of the audio stream(s) 126, the
example sound characteristic analyzer 202 identifies one or more
sound events 204 in the audio stream(s) 126. In some examples, the
sound characteristic analyzer 202 identifies a sound event 204
based on a change in one or more characteristics of the sound(s)
represented in the audio data, such as an increase in amplitude for
a period of time followed by a decrease in amplitude. In other
examples, the sound characteristic analyzer 202 identifies a sound
event 204 based on the characteristics of the audio data relative
to previously identified sound event(s) 204. The sound event(s) 204
can include a discrete sound event (e.g., an increase in amplitude
followed by a decrease in amplitude within a few seconds) or a
sound event occurring over, for example, a duration of the time
period for which the audio 110 is collected.
[0040] The sound impact analyzer 112 of the illustrated example
includes a sound comparer 206. The sound comparer 206 may be
implemented by a comparator or a processor programmed to perform a
comparison. The sound comparer 206 compares the sound event(s) 204
of the audio stream(s) 126 to predefined or reference sound data
208 for one or more sounds. In the example of FIG. 2, the reference
sound data 208 is stored in the database 200.
[0041] The sound comparer 206 compares the sound event(s) 204 of
the audio stream(s) 126 to the reference sound data 208 to
determine if, for example, the sound event(s) 204 in the audio 110
are sound event(s) to which the user has been previously exposed.
For example, the sound comparer 206 can identify a sound event 204
in the audio streams(s) 126 as an expected sound event for the
environment 102, such as traffic sounds for an outside environment
or machine sounds for a factory environment. Thus, the reference
sound data 208 provides a profile of expected or typical sound
events for a given environment. In some examples, the reference
sound data 208 is updated with known sound event(s) based on
analysis of the audio stream(s) 126 collected over time.
[0042] The example sound impact analyzer 112 includes a
physiological data analyzer 210. The physiological data analyzer
210 receives and/or otherwise retrieves the physiological response
data 130 collected by the sensor(s) 128 and processes the data. The
physiological data analyzer 210 can perform one or more operations
on the physiological response data 130 such as filtering the raw
signal data, removing noise from the signal data, converting the
signal data from analog data to digital data, and/or analyzing the
data.
[0043] The example physiological data analyzer 210 analyzes the
physiological response data 130 collected from the user (e.g., the
first user 104, the second user 106, and/or the third user 108) to
identify characteristics of and/or changes in the physiological
response data 130. For example, the physiological data analyzer 210
can analyze heart rate data collected from the user to determine a
resting heart rate for the user and/or to identify changes (e.g.,
sudden or abrupt changes and/or gradual changes) in the user's
heart rate. In some examples, the physiological data analyzer 210
compares the heart rate data to previously collected heart rate
data for the user (e.g., previously collected physiological
response data 130 stored in the database 200) to identify changes
in the user's heart rate data over time. The physiological data
analyzer 210 can analyze physiological response data 130 generated
by the sensor(s) 128 from measurements of other physiological
parameters such as blood pressure, respiration rate, skin
conductivity, etc.
[0044] Based on the analysis of the physiological response data
130, the physiological data analyzer 210 generates one or more
physiological events 212. In the example of FIG. 1, the
physiological events 212 are stored in the database 200 (e.g., a
database of physiological event(s) 212). In some examples, the
physiological event(s) 212 are based on discrete events in the
physiological response data 130, such as a sudden increase in heart
rate relative to a prior (e.g., the resting) heart rate for the
user followed by a return to the user's previous heart rate. In
other examples, the physiological event(s) 212 are indicative of
long-term or delayed change(s) in the physiological response data
130. Such long term changes are detected over time. An example of
such long term change is a gradual increase in the user's resting
heart rate (i.e., beats per minutes when the user is awake,
substantially relaxed, and not ill). In some examples, the
physiological event(s) 212 include short-term physiological events
and long-term physiological events for one or more physiological
parameters (e.g., heart rate, blood pressure, etc.).
[0045] The example sound impact analyzer 112 of FIG. 2 includes a
correlation identifier 214. In the illustrated example, the
correlation identifier 214 determines one or more correlations
between the sounds event(s) 204 identified by the sound
characteristic analyzer 202 and the physiological event(s) 212
identified by the physiological data analyzer 210. In some
examples, the correlation identifier 214 uses one or more
algorithms or correlation rules 216 stored in the database 200 to
identify correlation(s) between the sound event(s) 204 and the
physiological event(s) 212. The correlation rule(s) 216 can be
defined by one or more user inputs and/or developed automatically
over time based on a supervised or unsupervised machine learning
algorithm. The correlation rule(s) 216 can include, for example,
known correlations (e.g., a sudden, loud sound raises a user's
heart rate) or weighing factors (e.g., a rule that more weight
should be given to sound event(s) having a fast attack as compared
to a slow attack).
[0046] For example, the sound characteristic analyzer 202 can
identify a first sound event 204 indicating an increase in an
amplitude of the audio 110 followed by a decrease in the amplitude
at a first time T.sub.1. Also, the physiological data analyzer 210
can identify a first physiological event 212 indicating an increase
in the user's heart rate followed by a decrease in the user's heart
rate (e.g., a return to a prior heart rate). The physiological data
analyzer 210 can determine that the first physiological event 212
occurs at the first time T.sub.1+n, where n is an increment of time
(such as one second). Based on the identification of the first
sound event 204 occurring at the first time T.sub.1 and the first
physiological event 212 occurring at the first time T.sub.1+n, the
correlation identifier 214 determines that there is a correlation
(e.g., a causal connection) between the first sound event 204
(e.g., the increase in amplitude) and the first physiological event
212 (e.g., the increase in heart rate). In some examples, the
correlation identifier 214 identifies a correlation between the
first sound event 204 and the first physiological event 212 if the
first physiological event 212 occurs within a threshold time of the
first sound event 204 (e.g. T.sub.1+n) as defined by the
correlation rule(s) 216. The increment/threshold time n may be
different for different types of sound increments (e.g.,
milliseconds later in the context of a sudden, loud noise or sound
event, or within a 24-hour period of the occurrence in the case of
a substantial sound event).
[0047] In some examples, the sound event(s) 204 represent sound(s)
to which the user is repeatedly exposed to (e.g., every day) and/or
is exposed to over a duration of time surpassing a threshold (e.g.,
longer than an hour, longer than seven hours). In such examples,
the correlation identifier 214 evaluates the physiological event(s)
212 with respect to the cumulative exposure of the user to the
sound event(s) 204.
[0048] For example, the first sound event 204 can represent a sound
that occurs repeatedly within a first time period T.sub.1 (e.g., an
eight-hour time period). The first sound event 204 can be based on,
for example, the sound characteristic analyzer 202 detecting data
indicative of a high-pitch sound occurring repeatedly in the audio
stream(s) 126. A first physiological event 212 can indicate an
increase in the user's heart rate (e.g., based on historical
physiological response data 130) during the first period T.sub.1. A
second physiological event 212 can indicate an increase in the
user's heart rate relative to, for example, a resting heart rate
for the user during a second time period T.sub.2 different from the
first time period T.sub.1. The second time period T.sub.2 can
correspond to time when the user has departed the environment 102
but the physiological response data 130 is being collected from the
user.
[0049] In such examples, the example correlation identifier 214 of
FIG. 2 may identify a first correlation between the first sound
event 204 and the first physiological event 212. The correlation
identifier 214 can identify the first correlation based on, for
example, the occurrence of the first sound event 204 and the first
physiological event 212 during the first time period T.sub.1. The
first correlation can indicate that the user has a physiological
response (e.g., a sustained increased heart rate) due to the
repeated exposure to the first sound event 204 (e.g., the high
pitch sound event) during the first time period T.sub.1 (e.g., the
eight-hour time period).
[0050] Also, in such examples, the example correlation identifier
214 may identify a second correlation between the first sound event
204 and the second physiological event 212. For example, the
correlation identifier 214 may evaluate the second physiological
event 212 relative to other sound events 204 occurring during the
first time period T.sub.1 and/or the second time period T.sub.2 to
determine if the second physiological event 212 is related to
another sound event. The correlation identifier 214 may evaluate
other physiological events 212 occurring during the first and/or
second time periods T.sub.1, T.sub.2 relative to the first sound
event 204 and/or other sound events 204. In some examples, the
correlation identifier 214 gives more weight to the repeated nature
of the first sound event 204 during the first time period T.sub.1
as compared to other sound events that may only occur once during
the first time period T.sub.1 or the second time period T.sub.2.
Based on the analysis of the second physiological event 212
relative to other sounds events 204, the analysis of other
physiological events 212 for the user, and/or the weight given to
the repeated nature of the first sound event 204, the correlation
identifier 214 determines that there is a correlation between the
first sound event 204 and the second physiological event 212. Thus,
the correlation identifier 214 can identify physiological effects
of exposure to sound event(s) 204 that may appear in the
physiological response data 130 at a time after the occurrence(s)
of the sound event(s) 204 and/or after the user is removed from the
environment.
[0051] In some examples, the first sound event 204 occurs
repeatedly during the first time period T.sub.1 and each time the
user is in the environment 102 (e.g., five days a week). In some
such examples, the correlation identifier 214 determines that the
second physiological event 212 occurs each time or substantially
each time the user is exposed to the first sound event 204. The
correlation identifier 214 may determine that there is a
correlation between the first sound event 204 and the second
physiological event 212 in view of the repeated occurrence of the
second physiological event 212 when the first sound event 204
occurs over multiple data collection periods. Thus, the correlation
identifier 214 determines cumulative, long-term, and/or delayed
physiological effects of exposure to the sound event(s) on the
user, such as increased heart rate (which may be an indicator of
stress) for a user who works in a factory with loud machinery.
[0052] As another example, based on the characteristics of sound
events 204 identified by the sound characteristic analyzer 202
(e.g., attack, amplitude, duration) and the reference sound data
208, the sound comparer 206 may determine that the user is exposed
to loud voices as compared to average voice levels and more
frequently than expected. The physiological data analyzer 210 may
identify physiological events 212 for the user indicative of an
increased resting heart rate over time. The correlation identifier
214 may determine a correlation between the sound events 204 and
the physiological events 212 indicative of the effects of the loud
voices on the user.
[0053] In some examples, the correlation identifier 214 of FIG. 2
implements one or more machine learning algorithms (e.g.,
supervised learning algorithms). For example, the correlation
identifier 214 can learn physiological responses to one or more
sounds events 204 for a user based on the analysis of the
physiological response data 130 collected from the user over time.
In some examples, the correlation identifier 214 aggregates
physiological responses to one or more sound events collected from
two or more users (e.g., the first user 104, the second user 106,
and/or the third user 108 of FIG. 1). Based on the aggregated data,
the example correlation identifier 214 identifies trend(s) in the
physiological response(s) of user(s) to different sounds event(s)
204. In some examples, the correlation identifier 214 uses the
trend(s) to determine correlation(s) between the sound event(s) 204
having similar characteristics as the sound event(s) for which the
trend(s) were identified and the physiological event(s) 212 for one
or more users. Thus, the correlation identifier 214 identifies
correlation(s) based on machine learning algorithms with respect to
the sound event(s) 204 and the physiological event(s) 212. In some
examples, the correlation identifier 214 assigns strength level(s)
to the correlation(s) identified between the sound event(s) and the
physiological event(s) 212 (e.g., a strong correlation, a probable
correlation).
[0054] The example sound impact analyzer 112 of FIG. 2 includes a
filter adjuster 218. In some examples, the filter adjuster 218
evaluates the analysis of the sound event(s) 204 and the
physiological event(s) 212 performed by the correlation identifier
214 in view of one or more filter factors 220 stored in the
database 200. The filter adjuster 218 determines if the analysis
should account for one or more filter factors 220. The filter
factor(s) 220 can be defined based on one or more user inputs. The
filter analyzer 212 can consider, for example, attenuation, gain,
etc. with respect to the audio data corresponding to the sound
event(s) 204.
[0055] For example, a user input 127 may be received at the sound
impact analyzer 112 (e.g., via the first user application 120)
indicating that the user from which the physiological response data
130 is collected is wearing a noise reduction device such as ear
plugs. Based on a filter factor 220, the filter adjuster 218
recognizes that the ear plugs cause the user to hear sound
differently (e.g., at a reduced volume) than the sound
characteristics reflected in the audio stream 126 from the
microphone(s) 114. In such examples, the filter adjuster 218
communicates with the correlation identifier 214 regarding the
impact of the noise reduction device, such as reduced decibel
levels from the user's perspective. The example correlation
identifier 214 considers the use of the noise reduction device by
the user when determining the correlation(s) between the sound
event(s) 204 and the physiological event(s) 212. For example, the
correlation identifier 214 may determine that there is no
correlation between a sound event 204 and a physiological event 212
because the user was wearing ear plugs and, thus, was not exposed
to, or had limited exposure to, the sound event 204.
[0056] As another example, a first audio stream 126 collected by a
first microphone 114 of a first user device 116 (e.g., a
smartphone) may include audio data having a decibel level of 84 db.
A second audio stream 126 collected by a second microphone 114 of a
second user device 116 (e.g., a stand-alone speaker/audio sensor
device (e.g., Amazon.TM. Echo.TM.)) may include audio data having a
decibel level of 89 db. In this example, the first audio stream 126
and the second audio stream 126 are generated based on the same
audio 110 for the same time period. The example filter adjuster 218
determines that the decibel level of the audio data collected by
the first user device 116 is less than the decibel level of the
audio data collected by the second user device 116. Thus, the
filter adjuster 218 determines that the audio data of the first
audio stream 126 is attenuated relative to the audio data of the
second audio stream 126. For example, the first user device 116
(e.g., the smartphone) may be disposed in the user's pocket, a
purse, etc. and, thus, sounds detected by the microphone 114 may be
muted as compared to the sound detected by the second user device
116 (e.g., the stand-alone speaker/audio sensor device).
[0057] In other examples, the filter adjuster 218 determines that
the first audio stream 126 generated by the first user device 116
includes attenuated data based on a comparison of the sound
characteristics identified by the sound characteristic analyzer 202
for the audio data of the first audio stream 126 to the reference
sound data 208 stored in the database 200. For example, the filter
adjuster 218 can determine that the audio data is attenuated based
a comparison of the decibel level for the audio data to an expected
decibel level in the reference sound data 208.
[0058] If the filter adjuster 218 determines that the audio data in
the first audio stream 126 is attenuated, the filter adjuster 218
communicates with the correlation identifier 214. The correlation
identifier 214 accounts for the fact that the user may be exposed
to the audio 110 at, for example, a higher decibel level than
reflected in the first audio stream 126 when determining
correlations between the sound event(s) 204 and the physiological
event(s) 212. Thus, the filter adjuster 218 improves accuracy
and/or reduces errors in the analysis of the sound event(s) 204 and
the physiological event(s) 212 by the correlation identifier 214 by
accounting for factors such as placement of the microphone relative
to the user and/or the use of a noise reduction device by the
user.
[0059] The example sound impact analyzer 112 of FIG. 2 includes a
survey analyzer 222. The survey analyzer 222 analyzes the survey
data included in the user input(s) 127 collected from the user
(e.g., via the user device 116) in response to the survey(s) 134.
As disclosed above, the user input(s) 127 can include inputs from
user about the whether the user heard one or more sounds and/or the
user's psychological response to the sound(s) (e.g., level of
fright). The survey analyzer 222 tracks changes in the user's
responses over time. For example, the survey analyzer 222 may
determine that the user's hearing abilities have changed based on
an indication that the user no longer hears a sound he or she
previously indicated he or she heard. In other examples, the survey
analyzer 222 determines that the user's psychological response to
the sound has changed based on changes in the user's responses
regarding fright levels.
[0060] In some examples, the survey analyzer 222 communicates with
the correlation identifier 214 to assess or verify the correlations
identified by the correlation identifier 214 in view of survey
responses. For example, the correlation identifier 214 and/or the
survey analyzer 22 may confirm a correlation between a sound event
204 including an increase in amplitude and a physiological event
212 indicating an increase in the user's heart rate based on survey
respond data stating that the user was frightened when he or she
heard the sound corresponding to the sound event 204.
[0061] In some examples, the survey analyzer 222 adapts future
questions for the survey(s) 134 based on the user's responses. For
example, if the user indicates that he or she did not hear a sound
having a low frequency, such as a humming noise, the survey
analyzer 222 refrains from generating questions regarding the sound
in future survey(s) 134. In some examples, the adjustment of the
survey questions by the survey analyzer 222 is used to track
changes in the user's hearing ability and/or psychological
responses to the audio 110.
[0062] The example sound impact analyzer 112 of FIG. 2 includes a
crowd source analyzer 224. As disclosed above, in some examples,
the example sound impact analyzer 112 receives audio stream(s) 126
and physiological response data 130 for a plurality of users
associated with the environment 102 (e.g., the first user 104, the
second user 106, and/or the third user 108). In some examples, the
audio stream(s) 126 and the physiological response data 130 are
received in substantially real-time. In such examples, the sound
impact analyzer 112 analyzes the audio stream(s) 126 and the
physiological response data 130 received from the users in
substantially real-time to identify specific sound event(s) causing
similar physiological responses in the users. In other examples,
the analysis is not done in real-time or substantially
real-time.
[0063] For example, based on the audio streams 126, the sound
characteristic analyzer 202 may detect a sound event 204 based on
one or more characteristics of the audio data, such as attack,
duration, amplitude, etc. Also, the sound comparer 206 may
determine that the sound event is not a sound typically occurring
in the environment 102 based on a comparison of the sound event 204
to the reference sound data 208. Also, the physiological data
analyzer 210 may identify a physiological event 212 occurring in
two or more of the users. The physiological event 212 identified
for the users may be based on one or more similar changes in one or
more physiological parameters, such as an increase in heart rate
and/or respiration rate.
[0064] Based on the sound event 204 and the physiological event 212
identified from data collected from a plurality of users, the crowd
source analyzer 224 may determine that there has been a
crowd-impact event affecting the users. Based on the comparison of
the sound event 204 to the reference sound data 208 by the sound
comparer 206, the crowd source analyzer 224 may determine that the
crowd-impact event is a non-typical audio event for the
environment, such as an explosion. Thus, the crowd source analyzer
224 can identify events affecting a plurality of the users in the
environment in substantially real-time. Additionally or
alternatively, the crowd source analyzer 224 can identify trends in
the population with respect to physical and/or emotion health and
use those trends to recommend changes in the environment to reduce
any negative effects.
[0065] The example sound impact analyzer 112 of FIG. 2 includes a
report generator 226. Based on the correlations identified by the
correlation identifier 214, the analysis of the survey responses by
the survey analyzer 222, and/or the detection of a crowd-impact
event and/or trends by the crowd source analyzer 224, the report
generator 226 generates one or more of the reports 136 for output
by the sound impact analyzer 112.
[0066] For example, the report(s) 136 can include metrics regarding
a user's exposure to different sounds and/or recommendations for
audio levels of devices such as televisions. The recommendations
can be based on the physiological impact of other sounds on the
user in view of the correlations identified by the example
correlation identifier 214. The recommendations can include
preventive measures, such as a recommendation to lower television
volume levels at night in view of prolonged exposure to machine
sounds during the day and/or the user of ear protection
equipment.
[0067] In some examples, the report(s) 136 include data regarding
sound-generating sources in the environment 102. For example, the
report(s) 136 can identify noise sources in a building (e.g.,
elevators, equipment) based on the correlations between sound
event(s) detected in the building and physiological events detected
in multiple users indicative of, for example, a trend in stress
(e.g., increase heart rate). In some examples, specific noise
sources are identified based on data such as a location of the user
(e.g., based on GPS data) relative to the sources generating the
sound event(s) 204.
[0068] In some examples, the report(s) 136 include data regarding
the user's hearing capabilities and/or other physiological
parameters (e.g., heart rate, etc.). For example, the report(s) 136
can include data regarding the user's exposure to sound over time
and the user's responses to survey questions with respect to
whether he or she heard the sound, his or her reaction to the
sound, etc. As another example, the report(s) 136 can include
indicators that the user is experiencing stress due to exposure to
sound(s) based on analysis of heart rate data, blood pressure,
respiration, etc. The report(s) 136 may be shared with, for
example, the user and/or authorized medical personnel.
[0069] In some examples, the report(s) 136 include alerts to
authorized personnel such as a building managers or law enforcement
when the crowd source analyzer 224 detects a crowd-impact event
such as an explosion, loud crash, etc. Thus, the example sound
impact analyzer 112 can be used to provide data in, for example,
emergency situations based on real-time analysis of audio data. In
other examples, the report(s) 136 can be sent to government
agencies, physicians, researchers, etc. to facilitate public health
studies, OSHA regulations, and/or other activities.
[0070] In some examples, report generator 226 generates
instructions to be executed by one or more processors (e.g., the
processor 115 of the user device 116, the processor 129 of the
wearable device 118, the sound impact analyzer 112). For example,
the report(s) 136 can include instruction(s) for one or more sound
generating devices (e.g., machines) in the environment 102 to
reduce sound in the environment by automatically reducing and/or
ceasing operations. As another example, the report generator 226
can generate instruction(s) for an audio playing device in the
environment to automatically reduce an amplitude or decibel level
at which the device(s) play the audio 110. In examples where the
crowd source analyzer 224 identifies the occurrence of a
crowd-impact event such as an explosion, the report generator 226
may automatically generate a request for law enforcement
assistance, employer assistance, etc.
[0071] The report(s) 136 can include instruction(s) to
automatically place an order for noise protection devices such as
ear plugs or noise reduction head phones for the user(s) from an
online source (e.g., Amazon.TM.) or a local supplier to protect the
user. Such instruction(s) can be executed by the sound impact
analyzer 112 or any other processor (e.g., the processor 115 of the
user device 116)
[0072] The example sound impact analyzer of FIG. 2 includes a
communicator 228. The communicator 228 communicates with the report
presentation device(s) 138 (e.g., the user device(s) 116 of FIG. 1)
to deliver the report(s) 136 to local or remote report presentation
device(s) 138 for display, storage, and/or further analysis to
assess multiple environments to facilitate an industry-wide study,
etc. The communicator 228 communicates with the report presentation
device(s) 138 to execute the instruction(s) in the report(s) 136,
such as instructions to reduce or end operation of a machine to
reduce sound in the environment.
[0073] In some examples, the communicator 228 executes one or more
of the instructions generated by the report generator 226. For
example, in view of the detection of a crowd-impact event (e.g., an
explosive sound) by the crowd source analyzer 224, the communicator
228 can automatically transmit a request for law enforcement
assistance or employer assistance to the environment. As another
example, the communicator 228 can automatically place an order for
noise reduction device(s) (e.g., noise reduction headphones, ear
plugs, noise insulating materials) from an online or local supplier
to protect the user(s).
[0074] While an example manner of implementing the example sound
impact analyzer 112 is illustrated in FIGS. 1 and 2, one or more of
the elements, processes and/or devices illustrated in FIGS. 1 and 2
may be combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example database 200,
the example sound characteristic analyzer 202, the example sound
comparer 206, the example physiological data analyzer 210, the
example correlation identifier 214, the example correlation
identifier 214, the example filter adjuster 218, the example survey
analyzer 222, the example crowd source analyzer 224, the example
report generator 226, the example communicator 228 and/or, more
generally, the example sound impact analyzer 112 of FIGS. 1 and 2
may be implemented by hardware, software, firmware and/or any
combination of hardware, software and/or firmware. Thus, for
example, any of the example database 200, the example sound
characteristic analyzer 202, the example sound comparer 206, the
example physiological data analyzer 210, the example correlation
identifier 214, the example correlation identifier 214, the example
filter adjuster 218, the example survey analyzer 222, the example
crowd source analyzer 224, the example report generator 226, the
example communicator 228 and/or, more generally, the example sound
impact analyzer 112 of FIGS. 1 and 2 could be implemented by one or
more analog or digital circuit(s), logic circuits, programmable
processor(s), application specific integrated circuit(s) (ASIC(s)),
programmable logic device(s) (PLD(s)) and/or field programmable
logic device(s) (FPLD(s)). When reading any of the apparatus or
system claims of this patent to cover a purely software and/or
firmware implementation, at least one of the example database 200,
the example sound characteristic analyzer 202, the example sound
comparer 206, the example physiological data analyzer 210, the
example correlation identifier 214, the example correlation
identifier 214, the example filter adjuster 218, the example survey
analyzer 222, the example crowd source analyzer 224, the example
report generator 226, the example communicator 228 and/or, more
generally, the example sound analyzer 112 of FIGS. 1 and 2 is/are
hereby expressly defined to include a non-transitory computer
readable storage device or storage disk such as a memory, a digital
versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.
storing the software and/or firmware. Further still, the example
sound impact analyzer 112 of FIGS. 1 and 2 may include one or more
elements, processes and/or devices in addition to, or instead of,
those illustrated in FIGS. 1 and 2, and/or may include more than
one of any or all of the illustrated elements, processes and
devices.
[0075] A flowchart representative of example machine readable
instructions for implementing the example system 100 of FIGS. 1 and
2 is shown in FIG. 3. In this example, the machine readable
instructions comprise a program for execution by one or more
processors such as the processor 112 shown in the example processor
platform 400 discussed below in connection with FIG. 4. The program
may be embodied in software stored on a non-transitory computer
readable storage medium such as a CD-ROM, a floppy disk, a hard
drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory
associated with the processor 112, but the entire program and/or
parts thereof could alternatively be executed by a device other
than the processor 112 and/or embodied in firmware or dedicated
hardware. Further, although the example program is described with
reference to the flowchart illustrated in FIG. 3, many other
methods of implementing the example system 100 and/or components
thereof may alternatively be used. For example, the order of
execution of the blocks may be changed, and/or some of the blocks
described may be changed, eliminated, or combined.
[0076] As mentioned above, the example processes of FIG. 3 may be
implemented using coded instructions (e.g., computer and/or machine
readable instructions) stored on a non-transitory computer readable
storage medium such as a hard disk drive, a flash memory, a
read-only memory (ROM), a compact disk (CD), a digital versatile
disk (DVD), a cache, a random-access memory (RAM) and/or any other
storage device or storage disk in which information is stored for
any duration (e.g., for extended time periods, permanently, for
brief instances, for temporarily buffering, and/or for caching of
the information). As used herein, the term non-transitory computer
readable storage medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, "non-transitory computer readable storage medium" and
"non-transitory machine readable storage medium" are used
interchangeably.
[0077] FIG. 3 is a flowchart of example machine-readable
instructions that, when executed, cause the example sound impact
analyzer of FIGS. 1 and/or 2 to identify correlation(s) between
sound(s) in an environment (e.g., the environment 102 of FIG. 1)
and physiological response data collected from a user (e.g., the
first user 104, the second user 106, and/or the third user 108 of
FIG. 1) exposed to the environment. In the example of FIG. 3, the
physiological response data can be collected via sensor(s) 128 of
the wearable device 118 of FIG. 1. The sound(s) can be collected by
microphone(s) 114 (e.g., the microphone(s) of the user device(s)
116 and/or the wearable device(s) 118 of FIG. 1). The example
instructions of FIG. 3 can be executed by the sound impact analyzer
112 of FIGS. 1 and/or 2.
[0078] The example sound characteristic analyzer 202 of the sound
impact analyzer 112 of FIG. 2 accesses the audio stream(s) 126
including audio data collected from the environment (block 300).
The audio stream(s) 126 are generated by collecting sounds in the
environment via the microphone(s) 114 (e.g., of the user device(s)
116 and/or the wearable device(s) 118). The audio stream(s) 126 can
include, for example, audio data corresponding to traffic sounds,
machine operations, etc.
[0079] The example sound characteristic analyzer 202 identifies
sound event(s) 204 based on the audio data in the audio stream(s)
126 (block 302). For example, the sound characteristic analyzer 202
identifies one or more sound characteristics, such as amplitude,
frequency, pitch, duration, and/or attack. Based on the sound
characteristics and/or changes in the sound characteristics in the
audio stream(s) 126 relative to prior sound characteristics, the
example sound characteristic analyzer 202 identifies one or more
sound events 204. For example, a sound event 204 can include an
increase in amplitude of audio data followed by a decrease in the
amplitude in the audio data. In some examples, the sound event(s)
204 are analyzed in view of reference sound data 208 by the example
sound comparer 206 of FIG. 2 to classify the sounds event(s) 204
as, for example, expected sound event(s) 204 for the
environment.
[0080] The example physiological data analyzer 210 accesses the
physiological response data 130 collected from the user(s) exposed
to sounds in the environment (block 304). The physiological
response data 130 is obtained from the user(s) via the example
sensor(s) 128 of the wearable device(s) 118 of FIG. 1. The
physiological response data 130 can include, for example, heart
rate data, respiration rate data, skin conductivity data, etc.
[0081] The example physiological data analyzer 210 identifies
physiological event(s) 212 based on the physiological response data
130 (block 306). For example, the physiological data analyzer 210
identifies characteristics of and/or changes in one or more
physiological parameters (e.g., heart rate, respiration rate, etc.)
for the user(s). For example, the physiological data analyzer 210
can detect changes in a user's heart rate relative to a resting
heart rate for the user. In some examples, the physiological data
analyzer 210 identifies discrete or short-term physiological
event(s) 212, such as an increase in heart rate followed by a
return to a resting heart rate within a few minutes. In other
examples, the physiological data analyzer 210 identifies long-term
changes in a user's physiological responses, such as a sustained
increase in the user's heart rate relative to a prior heart
rate.
[0082] The example survey analyzer 222 accesses user input(s) 127
received at the sound impact analyzer 112 (block 308). The user
input(s) 127 can include psychological survey response data
received in response to, for example, survey(s) 134 presented to
the user(s) (e.g., via the user device(s) 116). For example, the
psychological survey response data can include responses indicating
how the sound(s) made the user(s) feel (e.g., frightened). The user
input(s) 127 can also include responses from the user(s) as to
whether the user(s) heard a particular sound, whether the user(s)
are wearing any noise reduction devices (e.g., ear plugs), etc.
[0083] The example correlation identifier 214 of the sound impact
analyzer 112 analyzes the sound event(s) 204 relative to the
physiological event(s) 212 for the user(s), the psychological
survey response data, and/or other user input(s) 127 based on one
or more supervised or unsupervised machine learning algorithms and,
in some examples, the filter factor(s) 220 (block 310). The
correlation identifier 214 uses the machine learning algorithms to
identify correlations between the sound event(s) 204 and the
physiological event(s) 212 to determine the physiological effects
of sound in the environment on the user(s). The correlation
identifier 214 can identify correlation(s) based on the correlation
rule(s) 216, which can include known correlations (e.g., a sudden,
loud sound raises a user's heart rate) or weighing factors (e.g., a
rule that more weight should be given to sound event(s) having a
long duration as compared to a short duration). The example
correlation identifier 214 learns physiological response(s) to
sound event(s) based on previously collected physiological response
data 130 and previously generated audio stream(s) 126. The example
correlation identifier 214 can identify correlations based on the
learned physiological responses based on, for example, similar
characteristics in the physiological response(s) and/or sound
event(s) 204 currently being analyzed by the correlation identifier
214. In some examples, the correlation identifier 214 identifies
correlation(s) between the sound event(s) 204 and the physiological
event(s) 212 based on a time of occurrence of the sound event(s)
204 relative to a time of occurrence of the physiological event(s)
212 (e.g., event(s) 204, 212 that occur within a threshold time
period of one another).
[0084] The example correlation identifier 214 can verify the
correlations between the sound event(s) 204 and the physiological
event(s) 212 based on the psychological survey response data
analyzed by the survey analyzer 222. In some examples, the
correlation identifier 214 determines that the sound event(s) 204
had a psychological effect on the user(s) based on the survey
response data. The survey analyzer 222 tracks user survey responses
over time and communicates with the correlation identifier 214 to
track, for example, changes in a user's ability to hear sound(s)
and/or changes in the user's psychological response to the sound
over time (e.g., based on fright levels). In some examples, the
correlation identifier adjust the correlation analysis based on
user input(s) 127 (e.g., to accurately correlate a physiological
and/or psychological response to a sound with a sound the user
confirmed he or she heard).
[0085] In some examples, the analysis of the sound event(s) 204 and
the physiological event(s) 212 by the correlation identifier 214
accounts for one or more filter factors 220 that may affect the
identification of the correlation(s). For example, the filter
adjuster 218 of the example sound impact analyzer 112 may determine
that the audio data collected by a user device 116 is attenuated
relative to audio data collected by another microphone enabled
device in the environment (e.g., a stand-alone speaker/audio sensor
device) and/or the reference sound data 208. Thus, the filter
adjuster 218 determines that the audio data does not accurately
represent the sound characteristics (e.g., amplitude levels) to
which the user is exposed. In such examples, the correlation
identifier 214 may adjust the correlation(s) to more accurately
reflect the user's physiological and/or psychological responses to
the sound event(s). In some examples, the filter adjuster 218
analyzes user input(s) 127 indicating that a user is wearing noise
reduction device(s) (e.g., ear plugs). In such examples,
correlation identifier 214 adjust may the correlation(s) to more
accurately reflect the user's physiological and/or psychological
responses to the sound event(s) in view of the effect(s) of the
noise reduction device(s) on the user's hearing.
[0086] If the correlation identifier 214 identifies correlation(s)
based on the sound event(s) 204, the physiological event(s) 212,
the psychological survey response data, and/or other user input(s)
127 (block 312), the example report generator 226 of the example
sound impact analyzer 112 generates one or more reports or
instructions 136 (block 314). The report(s) 136 can include, for
example, alert(s) to the user with respect to exposure to the sound
and/or recommendations for audio levels based on the user's
previous exposure to sound, physiological responses, and/or
psychological responses. In some examples, the report(s) 136
include data regarding the user's hearing capabilities and/or other
physiological parameters for delivery to the user and/or authorized
medical personnel. In some examples, the report(s) 136 include data
for a plurality of users in an environment with respect to
physiological and/or psychological effects of sounds from the
environment on the users. Such example report(s) 136 may be
delivered to, for example, a building manager to evaluate noise
sources from equipment in the building that are affecting multiple
users, government agencies for regulation-making purposes, etc.
[0087] The report(s) 136 can include instruction(s) to reduce sound
in the environment, such as an instruction for a machine to
automatically reduce or end operations or for an audio playing
device to automatically reduce a decibel level at which the audio
is played. The report(s) 136 can include instruction(s) for an
order for noise reduction device(s) (e.g., ear plugs) for the
user(s) to be automatically placed via an online or local supplier
(e.g., Amazon.TM.). The report(s) 136 can include request(s) that
are automatically transmitted to, for example, law enforcement, an
employer, etc., based on the detection of a crowd-impact event
(e.g., an explosion). The instruction(s) can be executed by the
communicator 228 of the example sound impact analyzer 112 and/or
communicated to one or more other processors (e.g., the processor
115 of the user device 116) for execution.
[0088] The example sound characteristic analyzer 202 of the example
sound impact analyzer 112 continues to analyze the audio stream(s)
126 with respect to identifying sounds event(s) if the audio
content stream(s) include additional audio data (block 316). If
there is no further audio data in the audio stream(s) 126, the
survey analyzer 222 determines whether further user input(s) 127 in
response to, for example, survey(s) 134, have been received at the
sound impact analyzer 112 (block 318). In some examples, the
user(s) are surveyed about their psychological responses to the
sound(s) after the audio data has been collected.
[0089] If there is no further audio data in the audio stream(s) 126
and no further user input(s) 127 are received by the sound impact
analyzer 112, the example physiological data analyzer 210
determines whether there are further physiological event(s) 212 in
the physiological response data 130 (block 320). In some examples,
the physiological event(s) 212 do not appear in the physiological
response data 130 until after the occurrence of the sound event(s)
204. For example, physiological changes such a sustained increased
heart rate relative to a prior heart rate may occur over time as a
result of repeated exposure to prolonged sounds (e.g., exposure to
machine sounds during the work day). In some examples, the
physiological event(s) occur after the user is removed from the
environment. Therefore, the physiological data analyzer 210
continues to analyze physiological response data 130 received from
the user(s) to identify physiological event(s) 212. Thus, the
correlation identifier 214 can identify correlation(s) between
sound event(s) and physiological event(s) based on proximity of the
event(s) 204, 212 in time (e.g., a loud sound caused an increase in
the user's heart rate at substantially the same time) and/or based
on the detection of delayed or long-term physiological event(s) 212
that may still stem from exposure to the sound event(s) 204.
[0090] If there is no further audio data, no further psychological
survey response data, and no further physiological response data to
be analyzed, the instruction of FIG. 3 end (block 322).
[0091] FIG. 4 is a block diagram of an example processor platform
400 capable of executing the instructions of FIG. 3 to implement
the example sound impact analyzer 112 of FIGS. 1 and/or 2. The
processor platform 400 can be, for example, a server, a personal
computer, a mobile device (e.g., a cell phone, a smart phone, a
tablet such as an iPad.TM.), a personal digital assistant (PDA), an
Internet appliance, a wearable device such as a watch, or any other
type of computing device.
[0092] The processor platform 400 of the illustrated example
includes a processor 112. The processor 112 of the illustrated
example is hardware. For example, the processor 112 can be
implemented by one or more integrated circuits, logic circuits,
microprocessors or controllers from any desired family or
manufacturer. In this example, the processor implements the sound
impact analyzer and its components (e.g., the example sound
characteristic analyzer 202, the example sound comparer 206, the
example physiological data analyzer 210, the example correlation
identifier 214, the example correlation identifier 214, the example
filter adjuster 218, the example survey analyzer 222, the example
crowd source analyzer 224, the example report generator 226, the
example communicator 228).
[0093] The processor 112 of the illustrated example includes a
local memory 413 (e.g., a cache). The processor 112 of the
illustrated example is in communication with a main memory
including a volatile memory 414 and a non-volatile memory 416 via a
bus 418. The volatile memory 414 may be implemented by Synchronous
Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory
(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any
other type of random access memory device. The non-volatile memory
416 may be implemented by flash memory and/or any other desired
type of memory device. Access to the main memory 414, 416 is
controlled by a memory controller. The database 200 of the sound
impact analyzer may be implemented by the main memory 414, 416.
[0094] The processor platform 400 of the illustrated example also
includes an interface circuit 420. The interface circuit 420 may be
implemented by any type of interface standard, such as an Ethernet
interface, a universal serial bus (USB), and/or a PCI express
interface.
[0095] In the illustrated example, one or more input devices 422
are connected to the interface circuit 420. The input device(s) 422
permit(s) a user to enter data and commands into the processor 112.
The input device(s) can be implemented by, for example, an audio
sensor, a microphone, a camera (still or video), a keyboard, a
button, a mouse, a touchscreen, a track-pad, a trackball, isopoint
and/or a voice recognition system.
[0096] One or more output devices 138, 424 are also connected to
the interface circuit 420 of the illustrated example. The output
devices 138, 424 can be implemented, for example, by display
devices (e.g., a light emitting diode (LED), an organic light
emitting diode (OLED), a liquid crystal display, a cathode ray tube
display (CRT), a touchscreen, a tactile output device, a printer
and/or speakers). The interface circuit 420 of the illustrated
example, thus, typically includes a graphics driver card, a
graphics driver chip or a graphics driver processor. Reports of the
report generator 226 may be exported on the interface circuit
420.
[0097] The interface circuit 420 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 426 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
[0098] The processor platform 400 of the illustrated example also
includes one or more mass storage devices 428 for storing software
and/or data. Examples of such mass storage devices 428 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD)
drives.
[0099] The coded instructions 432 of FIG. 3 may be stored in the
mass storage device 428, in the volatile memory 414, in the
non-volatile memory 1016, in the local memory 413, and/or on a
removable non-transitory computer readable storage medium such as a
CD or DVD.
[0100] From the foregoing, it will be appreciated that methods,
systems, and apparatus have been disclosed to determine biological
effects of sounds in an environment on individuals exposed to the
environment. Disclosed examples analyze sound(s) collected from the
environment via one or more microphones and physiological response
data (e.g., heart rate data, respiration rate data) collected from
the individuals exposed to the environment. In some examples, the
analysis is performed by cloud-based devices to facilitate
processing from multiple environments and/or multiple users.
Disclosed examples identify efficiently correlations between the
sound(s) and the physiological response data for an individual or
across two or more individuals using machine learning algorithms.
Disclosed example reduce errors in the correlations by accounting
for factors that may affect the collection of the sound(s) and/or
the user's exposure to the sound(s), such as placement of a
microphone in a user's pocket, the resulting attenuation of the
audio, and/or the use of noise reduction device(s) by the user.
[0101] Disclosed examples identify correlations between sound
events and physiological events in view of physiological responses
experienced by the user over time as a result of the exposure to
the sound events. Thus, disclosed examples intelligently identify
correlations that may not be directly time-based but, instead, are
indicative of long-term or delayed physiological effects to
exposure to the sounds. In some examples, users are surveyed about
their experiences in view of exposure to sounds to assess, for
example, psychological responses to the sounds or to track hearing
ability over time. Based on the analysis, disclosed examples
provide customized reports, alerts, etc. to the individuals and/or
other authorized users (e.g., medical personnel, researchers,
government agencies, etc.) with respect to exposure to sound(s) and
the biological effects of the sound exposure on the users.
[0102] The following is a non-exclusive list of examples disclosed
herein. Other examples may be included above. In addition, any of
the examples disclosed herein can be considered in whole or in
part, and/or modified in other ways.
[0103] Example 1 includes an apparatus including a sound
characteristic analyzer to identify a sound event based on audio
data collected in an environment. The apparatus includes a
physiological data analyzer to identify a physiological event based
on physiological response data collected from a user exposed to the
sound event in the environment. The apparatus includes a
correlation identifier to identify a correlation between the sound
event and the physiological event and a report generator to
generate a report based on the correlation.
[0104] Example 2 includes the apparatus as defined in example 1,
wherein the sound characteristic analyzer is to identify the sound
event based on a sound characteristic of the audio data.
[0105] Example 3 includes the apparatus as defined in example 2,
wherein the sound characteristic includes one or more of amplitude,
pitch, frequency, attack, or a duration of a sound in the audio
data.
[0106] Example 4 includes the apparatus as defined in examples 1 or
2, wherein the physiological response data includes one or more of
heart rate data, respiration rate data, blood pressure data, or
skin conductivity data.
[0107] Example 5 includes the apparatus as defined in example 1,
wherein the audio data is first audio data and the correlation
identifier is to perform a comparison of the first audio data to
second audio data and detect a change in a sound characteristic of
the first audio data relative to the sound characteristic in the
second audio data, the correlation identifier to identify the
correlation based on the change in the characteristic.
[0108] Example 6 includes the apparatus as defined in example 5,
wherein the correlation identifier is to identify an attenuation or
a gain of the first audio data relative to the second audio data
and adjust the correlation based on the attenuation or the
gain.
[0109] Example 7 includes the apparatus as defined in of any of
examples 1, 5, or 6, further including a filter adjuster to analyze
a user input indicating that the user employs a noise reduction
device.
[0110] Example 8 includes the apparatus as defined in examples 1 or
2, further including a survey analyzer to perform a comparison of
the correlation to a user input, the user input associated with the
sound event and verify the correlation based on the comparison.
[0111] Example 9 includes the apparatus as defined in any of
examples 1, 2, or 5, wherein the user is a first user, the
physiological event is a first physiological event, and the
correlation is a first correlation, the correlation identifier to
identify a second correlation between the sound event and a second
physiological event associated with a second user different from
the first user.
[0112] Example 10 includes the apparatus as defined in example 9,
wherein the report generator is to generate a first report based on
the first correlation and a second report based on the second
correlation.
[0113] Example 11 includes the apparatus as defined in example 10,
wherein at least one of the first report or the second report
includes a sound exposure alert for the user based on the
correlation.
[0114] Example 12 includes the apparatus as defined in example 9,
further including a sound comparer to perform a comparison of the
sound event to a reference sound event and a crowd source analyzer
to identify the sound event as affecting the first user and the
second user based on the first correlation, the second correlation,
and the comparison.
[0115] Example 13 includes the apparatus as defined in example 9,
wherein the report generator is to transmit a request to a third
party based on the identification of the sound event as affecting
the first user and the second user.
[0116] Example 14 includes the apparatus as defined in any of
examples 1, 2, or 5, wherein the sound event occurs at a first time
and the physiological event occurs at a second time, the second
time occurring after the first time.
[0117] Example 15 includes the apparatus as defined in any of
examples 1, 2, or 5, wherein the user is a first user, the
correlation identifier to identify the correlation based on
previously collected physiological response data for the first user
or for a second user.
[0118] Example 16 includes the apparatus as defined in example 15,
wherein the correlation identifier is to identify the correlation
based on a trend identified in first previously collected
physiological response data for the first user and second
previously collected physiological response data for the second
user relative to the sound event.
[0119] Example 17 includes the apparatus as defined in example 1,
wherein the report generator is to automatically place an order for
a noise reduction device for the user.
[0120] Example 18 includes a method including identifying, by
executing an instruction with a processor, a sound in an audio
stream collected in an environment. The method includes
identifying, by executing an instruction with the processor, a
physiological event based on physiological response data collected
from a user exposed to the sound in the environment. The method
includes determining, by executing an instruction with the
processor, a correlation between the sound and the physiological
event. The method includes generating, by executing an instruction
with the processor, a report based on the correlation.
[0121] Example 19 includes the method as defined in example 18,
further including identifying the sound based on a sound
characteristic of data in the audio stream.
[0122] Example 20 includes the method as defined in example 19,
wherein the sound characteristic includes one or more of amplitude,
pitch, frequency, attack, or a duration of a sound in the data.
[0123] Example 21 includes the method as defined in examples 18 or
19, wherein the physiological response data includes one or more of
heart rate data, respiration rate data, blood pressure data, or
skin conductivity data.
[0124] Example 22 includes the method as defined in example 18,
wherein the audio stream is a first audio stream and further
including performing a comparison of the first audio stream to a
second audio stream. The method includes detecting a change in a
sound characteristic of the first audio stream relative to the
sound characteristic in the second audio stream. The method
includes identifying the correlation based on the change in the
characteristic.
[0125] Example 23 includes the method as defined in example 22,
wherein the sound characteristic is a decibel level and further
including measuring a first decibel level of the first audio stream
and a second decibel level of the second audio stream. The method
includes identifying an attenuation or a gain of the first decibel
level relative to the second decibel level. The method includes
adjusting the correlation based on the attenuation or the gain.
[0126] Example 24 includes the method as defined in any of examples
18, 22, or 23, further including analyzing a user input indicating
that the user employs a noise reduction device and adjusting the
correlation based on the user input.
[0127] Example 25 includes the method as defined in examples 18 or
19, further including verifying the correlation based on a user
input received in response to the sound.
[0128] Example 26 includes the method as defined in any of examples
18, 19, or 22, wherein the user is a first user, the physiological
event is a first physiological event, and the correlation is a
first correlation, further including identifying a second
correlation between the sound and a second physiological event
associated with a second user different from the first user.
[0129] Example 27 includes the method as defined in example 26,
wherein generating the report includes generating a first report
based on the first correlation and a second report based on the
second correlation.
[0130] Example 28 includes the method as defined in example 27,
wherein at least one of the first report or the second report
includes an instruction for a sound generating device to reduce an
amplitude of the sound.
[0131] Example 29 includes the method as defined in example 26,
further including performing a comparison of the sound to a
reference sound and identifying the sound as affecting the first
user and the second user based on the first correlation, the second
correlation, and the comparison.
[0132] Example 30 includes the method as defined in example 26,
further including transmitting a request to a third party based on
the identification of the sound as affecting the first user and the
second user.
[0133] Example 31 includes the method as defined in any of examples
18, 19, or 22, wherein the sound occurs at a first time and the
physiological event occurs at a second time, the second time
occurring after the first time.
[0134] Example 32 includes the method as defined in any of examples
18, 19, or 22, wherein the user is a first user and further
including identifying the correlation based on previously collected
physiological response data for the first user or for a second
user.
[0135] Example 33 includes the method as defined in example 32,
further including identifying the correlation based on a trend
identified in first previously collected physiological response
data for the first user and second previously collected
physiological response data for the second user relative to the
sound.
[0136] Example 34 includes the method as defined in example 18,
wherein generating the report includes automatically placing an
order for a noise reduction device for the user.
[0137] Example 35 includes at least one computer readable storage
medium comprising instructions that, when executed, cause a machine
to at least detect a sound event in audio data collected in an
environment, detect a physiological event in physiological response
data collected from a user exposed to the sound event in the
environment, identify a correlation between the sound event and the
physiological event, and generate an instruction based on the
correlation.
[0138] Example 36 includes the at least one computer readable
storage medium as defined in example 35, wherein the instructions,
when executed, further cause the machine to identify the sound
event based on a sound characteristic of the audio data.
[0139] Example 37 includes the at least one computer readable
storage medium as defined in example 36, wherein the sound
characteristic includes one or more of amplitude, pitch, frequency,
attack, or a duration of a sound in the audio data.
[0140] Example 38 includes the at least one computer readable
storage medium as defined in examples 35 or 36, wherein the
physiological response data includes one or more of heart rate
data, respiration rate data, blood pressure data, or skin
conductivity data.
[0141] Example 39 includes the at least one computer readable
storage medium as defined in example 35, wherein the audio data is
first audio data and instructions, when executed, further cause the
machine to perform a comparison of the first audio data to second
audio data, detect a change in a sound characteristic of the first
audio data relative to the sound characteristic in the second audio
data, and identify the correlation based on the change in the
characteristic.
[0142] Example 40 includes the at least one computer readable
storage medium as defined in example 39, wherein the instructions,
when executed, further cause the machine to identify an attenuation
or a gain of the first audio data relative to the second audio data
and adjust the correlation based on the attenuation or the
gain.
[0143] Example 41 includes the at least one computer readable
storage medium as defined in any of examples 35, 39, or 40, wherein
the instructions, when executed, further cause the machine to
analyze a user input indicating that the user employs a noise
reduction device and adjust the correlation based on the user
input.
[0144] Example 42 includes the at least one computer readable
storage medium as defined in examples 35 or 36, wherein the
instructions, when executed, further cause the machine to verify
the correlation based on a user input associated with the sound
event.
[0145] Example 43 includes the at least one computer readable
storage medium as defined in any of examples 35, 36, or 39, wherein
the user is a first user, the physiological event is a first
physiological event, and the correlation is a first correlation,
and wherein the instructions, when executed, further cause the
machine to identify a second correlation between the sound event
and a second physiological event associated with a second user
different from the first user.
[0146] Example 44 includes the at least one computer readable
storage medium as defined in example 43, wherein the instructions,
when executed, further cause the machine to generate a first
instruction based on the first correlation and a second instruction
based on the second correlation.
[0147] Example 45 includes the at least one computer readable
storage medium as defined in example 44, wherein at least one of
the first instruction or the second instruction includes an
instruction for a sound generating device to reduce an amplitude of
the sound.
[0148] Example 46 includes the at least one computer readable
storage medium as defined in example 45, wherein the instructions,
when executed, further cause the machine to perform a comparison of
the sound event to a reference sound event and determine that the
sound event affects the first user and the second user based on the
first correlation, the second correlation, and the comparison.
[0149] Example 47 includes the at least one computer readable
storage medium as defined in example 44, wherein the instructions,
when executed, further cause the machine to generate the
instruction by transmitting a request to a third party based on the
identification of the sound event as affecting the first user and
the second user.
[0150] Example 48 includes the at least one computer readable
storage medium as defined in any of examples 35, 36, or 39, wherein
the sound event occurs at a first time and the physiological event
occurs at a second time, the second time occurring after the first
time.
[0151] Example 49 includes the at least one computer readable
storage medium as defined in any of examples 35, 36, or 39, wherein
the user is a first user and wherein the instructions, when
executed, further cause the machine to identify the correlation
based on previously collected physiological response data for the
first user or for a second user.
[0152] Example 50 includes the at least one computer readable
storage medium as defined in example 49, wherein the instructions,
when executed, further cause the machine to identify the
correlation based on a trend identified in first previously
collected physiological response data for the first user and second
previously collected physiological response data for the second
user relative to the sound event.
[0153] Example 51 includes the at least one computer readable
storage medium as defined in example 35, wherein the instructions,
when executed, further cause the machine to generate the
instruction by automatically placing an order for a noise reduction
device for the user.
[0154] Example 52 includes an apparatus including means for
identifying a sound event based on audio data collected in an
environment, means for identifying a physiological event based on
physiological response data collected from a user exposed to the
sound event in the environment, means for identifying a correlation
between the sound event and the physiological event, and means for
generating an instruction based on the correlation.
[0155] Example 53 includes the apparatus as defined in claim 52,
wherein the instruction includes one or more of an order for a
noise reduction device for the user, a command for a sound
generating device to reduce an amplitude of the sound, or an alert
for a third party.
[0156] Example 54 includes the apparatus as defined in claim 52,
wherein the audio data is first audio data and further including
means for identifying an attenuation of the first audio data
relative to second audio data, the means for identifying the
correlation to adjust the correlation based on the attenuation.
[0157] Although certain example methods, apparatus and articles of
manufacture have been disclosed herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the claims of this patent.
* * * * *